Open Mind Common Sense: Knowledge Acquisition from the General Public

By Push Singh, Thomas Lin, Erik T. Mueller, Grace Lim, Travell Perkins, W. Zhu. 2002

600 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Knowledge Graphs & Representation

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Citations in the corpus

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021, methodology): “For the ConceptNet dataset, LAMA uses the gold Open Mind Common Sense (OMCS) dataset (Singh et al., 2002).”
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020, background): “, 2020) finds that the OMCS corpus (Singh et al., 2002), which has derived the ConceptNet, is a valuable resource for retrieving relevant commonsense facts for discriminative reasoning about questions.”
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021, background): “For example, ConceptNet originated from the Open Mind Common Sense project that collects commonsense statements online from web users (Singh et al., 2002) 1 and GenericsKB consists of crawled text from public websites.”
Representing General Relational Knowledge in ConceptNet 5 (2012, background): “ConceptNet originated as a representation for the knowledge collected by the Open Mind Common Sense project (Singh et al., 2002), which uses a long-running interactive Web site to collect new statements from visitors to the site, and asks them target questions about statements it thinks may be true.”
Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015, background): “Most existing common sense knowledge bases rely on knowledge described via text – either mined [6, 26, 31] or manually entered [37, 44, 5, 45].”
Birds Have Four Legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models (2020, methodology): “To build a suitable dataset for the proposed probing task, we make use of an existing corpus consisting of commonsense assertions, named Open Mind Common Sense (OMCS) (Singh et al., 2002).”
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge (2018, methodology): “In our experiments we use knowledge from the Open Mind Common Sense (OMCS, Singh et al. (2002)) part of ConceptNet, a crowd-sourced resource of commonsense knowledge with a total of ∼630k facts.”
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (2018, methodology): “As a starting point for our data collection, we use 1 www.mturk.com scenarios from three script data collections (Regneri et al., 2010; Singh et al., 2002; Wanzare et al., 2016).”
ASER: A Large-scale Eventuality Knowledge Graph (2019, background): “We study the relationship between ASER and the commonsense knowledge in ConceptNet [25], or previously called Open Mind Common Sense (OMCS) [38].”
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension (2018, background): “A large portion of previous work focus on commonsense knowledge acquisition with unsupervised learning (Chambers and Jurafsky, 2008; Tandon et al., 2017) or crowdsourcing approach (Singh et al., 2002; Wanzare et al., 2016).”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, methodology): “While the first version mainly relied on data from the Open Mind Common Sense project [90], now ConceptNet5’s sources range “from dictionaries to online games” [91], including DBPedia [7], English Wiktionary15, and WordNet 3.”
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers (2020, background): “, 2017) and the Open Mind Common Sense (OMCS) corpus (Singh et al., 2002), from which the ConceptNet graph was (semi-)automatically extracted.”
Augmenting End-to-End Dialogue Systems With Commonsense Knowledge (2018, background): “Based on the Open Mind Common Sense project (Singh et al. 2002), ConceptNet not only contains objective facts such as “Paris is the capital of France” that are constantly true, but also captures informal relations between common concepts that are part of everyday knowledge such as “A dog is a pet”.”
Probase: a probabilistic taxonomy for text understanding (2012, neutral): “Freebase [5] 1,450 WordNet [13] 25,229 WikiTaxonomy [26] 111,654 YAGO [35] 352,297 DBPedia [1] 259 ResearchCyc [18] ≈ 120,000 KnowItAll [12] N/A TextRunner [2] N/A OMCS [31] N/A NELL [7] 123”
Human computation: a survey and taxonomy of a growing field (2011, background): “Several efforts have demonstrated ways of using volunteer contributors to provide such facts, either by using games (e.g. the FACTory [60], Verbosity [67], 1001 Paraphrases [12]) or by plain volunteerism (e.g. Learner [11], Open Mind Common Sense [58]).”
Commonsense Reasoning in and Over Natural Language (2004, background): “ConceptNet is a machine-usable resource mined out of the Open Mind Commonsense (OMCS) corpus (Singh et al. 2002), a collection of nearly 700,000 English sentences of commonsense facts collected from over 14,000 contributors from around the world, over the past three years.”